The vision of our research is to enable robots to function in dynamic human
environments by allowing them to flexibly adapt their skill set via learning
interactions with end-users. We call this Socially Guided Machine Learning (SG-ML),
exploring the ways in which Machine Learning agents can exploit principles of
human social learning. To date, our work in SG-ML has focused on two research thrusts:
(1) Interactive Machine Learning, and (2) Natural Interaction Patterns for HRI.
Here you will find recent examples of projects in each of these two thrusts.

Interactive Machine Learning

Embodied Active Learning Queries
M. Cakmak, A.L. Thomaz

Programming new skills on a robot should take minimal time and effort.
One approach to achieve this goal is to allow the robot to ask questions (called Active Learning).
In this work, we identify three types of questions (label, demonstration and feature queries) and
show how a robot can use these "Embodied Queries" while learning new skills from demonstration.

Kinesthetic teaching is an approach to LfD where a human physically guides
a robot to perform a skill. In the common usage,
the robot’s trajectory during a demonstration is recorded from start to end.
We propose an alternative, keyframe demonstrations, in which the human
provides a sparse set of consecutive keyframes that can be connected to
perform the skill. We have presented a user-study comparing the two approaches
and highlighting their complementary nature. Thus, we introduce a hybrid method
that combines trajectories and keyframes in a single demonstration, and present a learning
framework that can handle all three types of input.

We are investigating some of the problems that arise when using active learning
in the context of human–robot interaction (HRI).
In experiments with human subjects we have explored three different versions of
mixed-initiative active learning, and shown they are all preferable to passive
supervised learning. But issues arrise around balance of control, compliance
to queries, and perceived utility of the questions.

In this project a social
robot learns task goals from human demonstrations without prior
knowledge of high-level concepts. New
concepts are grounded from low-level continuous sensor data through
unsupervised learning, and task goals are subsequently learned
using a Bayesian approach. These concepts can be used to transfer knowledge
to future tasks, resulting in faster learning of those tasks.

Learning about Objects from Humans
M. Cakmak, A.L. Thomaz

A general learning task for a robot in a new environment is to learn about objects and what actions/eﬀects
they aﬀord. To approach this, we look at ways that a human partner can intuitively help the robot learn,
Socially Guided Machine Learning. We conducted experiments with our robot, Junior, and made six
observations characterizing how people approached teaching about objects. We showed that Junior successfully
used transparency to mitigate errors. Finally, we present the impact of “social” versus “non-social” data sets
when training SVM classiﬁers.

"Social" learning in robotics has focused on imitation learning, but
we take a broader view and are interested in the multifaceted
ways that a social partner can inﬂuence the learning process. We implement stimulus
enhancement, emulation, mimicking and imiation on a robot,
and illustrate the computational
beneﬁts of social learning over self exploration.
Additionally we characterize the differences between strategies, showing that the
preferred strategy is dependent on the environment and the behavior of the
social partner.

We are interested in machines that can learn from everyday people. To study this,
we are building a suite of short computer games, with interactive learning agents.
These serve as a testbed for experiments with various algorithms and interface techniques, looking at how to
allow the average person to successfully teach machine learning agents.

Sophie's Kitchen is work from Prof. Thomaz' PhD thesis at MIT with Cynthia Breazeal.
This is an environment to experiment with Interactive Reinforcement Learning. You can find
out more about the Sophie project, and teach Sophie to bake a cake, at the
Sophie's Kitchen demo page.

Natural Interaction Patterns for HRI

Multimodal Turn-taking for HRI
C. Chao, A. L. Thomaz

If we want robots to engage effectively with humans on a daily basis
in service applications or in collaborative work scenarios, then it
will become increasingly important for them to achieve the type of
interaction fluency that comes naturally between humans. In this work
we are developing an autonomous robot controller for multi-modal
reciprocal turn-taking interactions, allowing a robot to
better manage how they time their actions with a human partner.

We are developing novel methods for detecting a contingent response
by a human to the stimulus of a robot action. Contingency is defined as a change
in an agent’s behavior within a specific time window in direct response to a signal
from another agent; detection of such responses is essential to assess the willingness
and interest of a human in interacting with the robot.

Life-like Robot Motion
M.Gielniak, C.K. Liu, A.L.Thomaz

We hypothesize that believable "human-like" motion increases communication,
improves interaction, and advances task completion for social robots interacting
with human partners.
In this work we explore the interaction benefits gained when robots communicate
with their partners using a familiar way: robot motion that is human-like.
This has two concrete goals: (1) synthesize robot motion that is more human-like,
and (2) add communication to benefit interaction.

One contribution of our research has been showing motor coordination
(i.e. spatiotemporal correspondence) to be a metric for believable motion;
We use this to develop a real-time, dynamic, autonomous motion algorithm,
which systematically composes communicative signals to robot motion using minimal
prior information.

Additionally we have introduced algorithms for three specific methods of communicating
via motion (i.e. secondary motion, exaggeration, and anticipation).